{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exogenous Variables"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exogenous variables can provide additional information to greatly improve forecasting accuracy. Some examples include price or future promotions variables for demand forecasting, and weather data for electricity load forecast. In this notebook we show an example on how to add different types of exogenous variables to NeuralForecast models for making day-ahead hourly electricity price forecasts (EPF) for France and Belgium markets."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All NeuralForecast models are capable of incorporating exogenous variables to model the following conditional predictive distribution:\n",
    "$$\\mathbb{P}(\\mathbf{y}_{t+1:t+H} \\;|\\; \\mathbf{y}_{[:t]},\\; \\mathbf{x}^{(h)}_{[:t]},\\; \\mathbf{x}^{(f)}_{[:t+H]},\\; \\mathbf{x}^{(s)} )$$\n",
    "\n",
    "where the regressors are static exogenous $\\mathbf{x}^{(s)}$, historic exogenous $\\mathbf{x}^{(h)}_{[:t]}$, exogenous available at the time of the prediction $\\mathbf{x}^{(f)}_{[:t+H]}$ and autorregresive features $\\mathbf{y}_{[:t]}$. Depending on the [train loss](https://nixtla.github.io/neuralforecast/losses.pytorch.html), the model outputs can be point forecasts (location estimators) or uncertainty intervals (quantiles)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will show you how to include exogenous variables in the data, specify variables to a model, and produce forecasts using future exogenous variables."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "This Guide assumes basic knowledge on the NeuralForecast library. For a minimal example visit the [Getting Started](./Getting_Started.ipynb) guide.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Exogenous_Variables.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `df` dataframe contains the target and exogenous variables past information to train the model. The `unique_id` column identifies the markets, `ds` contains the datestamps, and `y` the electricity price.\n",
    "\n",
    "Include both historic and future temporal variables as columns. In this example, we are adding the system load (`system_load`) as historic data. For future variables, we include a forecast of how much electricity will be produced (`gen_forecast`) and day of week (`week_day`). Both the electricity system demand and offer impact the price significantly, including these variables to the model greatly improve performance, as we demonstrate in Olivares et al. (2022). \n",
    "\n",
    "The distinction between historic and future variables will be made later as parameters of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>gen_forecast</th>\n",
       "      <th>system_load</th>\n",
       "      <th>week_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 00:00:00</td>\n",
       "      <td>53.48</td>\n",
       "      <td>76905.0</td>\n",
       "      <td>74812.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 01:00:00</td>\n",
       "      <td>51.93</td>\n",
       "      <td>75492.0</td>\n",
       "      <td>71469.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 02:00:00</td>\n",
       "      <td>48.76</td>\n",
       "      <td>74394.0</td>\n",
       "      <td>69642.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "      <td>42.27</td>\n",
       "      <td>72639.0</td>\n",
       "      <td>66704.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 04:00:00</td>\n",
       "      <td>38.41</td>\n",
       "      <td>69347.0</td>\n",
       "      <td>65051.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id                  ds      y  gen_forecast  system_load  week_day\n",
       "0        FR 2015-01-01 00:00:00  53.48       76905.0      74812.0         3\n",
       "1        FR 2015-01-01 01:00:00  51.93       75492.0      71469.0         3\n",
       "2        FR 2015-01-01 02:00:00  48.76       74394.0      69642.0         3\n",
       "3        FR 2015-01-01 03:00:00  42.27       72639.0      66704.0         3\n",
       "4        FR 2015-01-01 04:00:00  38.41       69347.0      65051.0         3"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE.csv')\n",
    "df['ds'] = pd.to_datetime(df['ds'])\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "Calendar variables such as day of week, month, and year are very useful to capture long seasonalities.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(df[df['unique_id']=='FR']['ds'], df[df['unique_id']=='FR']['y'])\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price [EUR/MWh]')\n",
    "plt.grid()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the static variables in a separate `static_df` dataframe. In this example, we are using one-hot encoding of the electricity market. The `static_df` must include one observation (row) for each `unique_id` of the `df` dataframe, with the different statics variables as columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>market_0</th>\n",
       "      <th>market_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FR</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BR</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id  market_0  market_1\n",
       "0        FR         1         0\n",
       "1        BR         0         1"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "static_df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE_static.csv')\n",
    "static_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Training with exogenous variables"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We distinguish the exogenous variables by whether they reflect static or time-dependent aspects of the modeled data.\n",
    "\n",
    "* **Static exogenous variables**: \n",
    "The static exogenous variables carry time-invariant information for each time series. When the model is built with global parameters to forecast multiple time series, these variables allow sharing information within groups of time series with similar static variable levels. Examples of static variables include designators such as identifiers of regions, groups of products, etc.\n",
    "\n",
    "* **Historic exogenous variables**:\n",
    "This time-dependent exogenous variable is restricted to past observed values. Its predictive power depends on Granger-causality, as its past values can provide significant information about future values of the target variable $\\mathbf{y}$.\n",
    "\n",
    "* **Future exogenous variables**: \n",
    "In contrast with historic exogenous variables, future values are available at the time of the prediction. Examples include calendar variables, weather forecasts, and known events that can cause large spikes and dips such as scheduled promotions."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To add exogenous variables to the model, first specify the name of each variable from the previous dataframes to the corresponding model hyperparameter during initialization: `futr_exog_list`, `hist_exog_list`, and `stat_exog_list`. We also set `horizon` as 24 to produce the next day hourly forecasts, and set `input_size` to use the last 5 days of data as input.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuralforecast.auto import NHITS, BiTCN\n",
    "from neuralforecast.core import NeuralForecast\n",
    "\n",
    "import logging\n",
    "logging.getLogger(\"pytorch_lightning\").setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ospra\\miniconda3\\envs\\neuralforecast\\lib\\site-packages\\pytorch_lightning\\utilities\\parsing.py:199: Attribute 'loss' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss'])`.\n",
      "Seed set to 1\n",
      "Seed set to 1\n"
     ]
    }
   ],
   "source": [
    "horizon = 24 # day-ahead daily forecast\n",
    "models = [NHITS(h = horizon,\n",
    "                input_size = 5*horizon,\n",
    "                futr_exog_list = ['gen_forecast', 'week_day'], # <- Future exogenous variables\n",
    "                hist_exog_list = ['system_load'], # <- Historical exogenous variables\n",
    "                stat_exog_list = ['market_0', 'market_1'], # <- Static exogenous variables\n",
    "                scaler_type = 'robust'),\n",
    "          BiTCN(h = horizon,\n",
    "                input_size = 5*horizon,\n",
    "                futr_exog_list = ['gen_forecast', 'week_day'], # <- Future exogenous variables\n",
    "                hist_exog_list = ['system_load'], # <- Historical exogenous variables\n",
    "                stat_exog_list = ['market_0', 'market_1'], # <- Static exogenous variables\n",
    "                scaler_type = 'robust',\n",
    "                ),                \n",
    "                ]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "When including exogenous variables always use a scaler by setting the `scaler_type` hyperparameter. The scaler will scale all the temporal features: the target variable `y`, historic and future variables.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "Make sure future and historic variables are correctly placed. Defining historic variables as future variables will lead to data leakage.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, pass the datasets to the `df` and `static_df` inputs of the `fit` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "nf = NeuralForecast(models=models, freq='H')\n",
    "nf.fit(df=df,\n",
    "       static_df=static_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Forecasting with exogenous variables"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before predicting the prices, we need to gather the future exogenous variables for the day we want to forecast. Define a new dataframe (`futr_df`) with the `unique_id`, `ds`, and future exogenous variables. There is no need to add the target variable `y` and historic variables as they won't be used by the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>gen_forecast</th>\n",
       "      <th>week_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 00:00:00</td>\n",
       "      <td>49118.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 01:00:00</td>\n",
       "      <td>47890.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 02:00:00</td>\n",
       "      <td>47158.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 03:00:00</td>\n",
       "      <td>45991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 04:00:00</td>\n",
       "      <td>45378.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id                  ds  gen_forecast  week_day\n",
       "0        FR 2016-11-01 00:00:00       49118.0         1\n",
       "1        FR 2016-11-01 01:00:00       47890.0         1\n",
       "2        FR 2016-11-01 02:00:00       47158.0         1\n",
       "3        FR 2016-11-01 03:00:00       45991.0         1\n",
       "4        FR 2016-11-01 04:00:00       45378.0         1"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futr_df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE_futr.csv')\n",
    "futr_df['ds'] = pd.to_datetime(futr_df['ds'])\n",
    "futr_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "Make sure `futr_df` has informations for the entire forecast horizon. In this example, we are forecasting 24 hours ahead, so `futr_df` must have 24 rows for each time series.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, use the `predict` method to forecast the day-ahead prices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ospra\\miniconda3\\envs\\neuralforecast\\lib\\site-packages\\utilsforecast\\processing.py:352: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
      "  freq = pd.tseries.frequencies.to_offset(freq)\n",
      "c:\\Users\\ospra\\miniconda3\\envs\\neuralforecast\\lib\\site-packages\\utilsforecast\\processing.py:404: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
      "  freq = pd.tseries.frequencies.to_offset(freq)\n",
      "c:\\Users\\ospra\\OneDrive\\Phd\\Repositories\\neuralforecast\\neuralforecast\\tsdataset.py:91: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  self.temporal = torch.tensor(temporal, dtype=torch.float)\n",
      "c:\\Users\\ospra\\OneDrive\\Phd\\Repositories\\neuralforecast\\neuralforecast\\tsdataset.py:95: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  self.static = torch.tensor(static, dtype=torch.float)\n",
      "c:\\Users\\ospra\\miniconda3\\envs\\neuralforecast\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:441: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=19` in the `DataLoader` to improve performance.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "35847892c983422d96ad9e8afee27afb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cf2fb8d84e94f5e831826ca0f8362e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ospra\\OneDrive\\Phd\\Repositories\\neuralforecast\\neuralforecast\\core.py:179: FutureWarning: In a future version the predictions will have the id as a column. You can set the `NIXTLA_ID_AS_COL` environment variable to adopt the new behavior and to suppress this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>BiTCN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 00:00:00</td>\n",
       "      <td>38.138920</td>\n",
       "      <td>41.105774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 01:00:00</td>\n",
       "      <td>34.647514</td>\n",
       "      <td>35.589905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 02:00:00</td>\n",
       "      <td>33.428795</td>\n",
       "      <td>33.034309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 03:00:00</td>\n",
       "      <td>32.428146</td>\n",
       "      <td>30.183418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 04:00:00</td>\n",
       "      <td>31.068453</td>\n",
       "      <td>29.396011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ds      NHITS      BiTCN\n",
       "unique_id                                          \n",
       "BE        2016-11-01 00:00:00  38.138920  41.105774\n",
       "BE        2016-11-01 01:00:00  34.647514  35.589905\n",
       "BE        2016-11-01 02:00:00  33.428795  33.034309\n",
       "BE        2016-11-01 03:00:00  32.428146  30.183418\n",
       "BE        2016-11-01 04:00:00  31.068453  29.396011"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = nf.predict(futr_df=futr_df)\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plot_df = df[df['unique_id']=='FR'].tail(24*5).reset_index(drop=True)\n",
    "Y_hat_df = Y_hat_df.reset_index(drop=False)\n",
    "Y_hat_df = Y_hat_df[Y_hat_df['unique_id']=='FR']\n",
    "\n",
    "plot_df = pd.concat([plot_df, Y_hat_df ]).set_index('ds') # Concatenate the train and forecast dataframes\n",
    "\n",
    "plot_df[['y', 'NHITS', 'BiTCN']].plot(linewidth=2)\n",
    "plt.axvline('2016-11-01', color='red')\n",
    "plt.ylabel('Price [EUR/MWh]', fontsize=12)\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "plt.grid()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In summary, to add exogenous variables to a model make sure to follow the next steps:\n",
    "\n",
    "1. Add temporal exogenous variables as columns to the main dataframe (`df`).\n",
    "2. Add static exogenous variables with the `static_df` dataframe.\n",
    "3. Specify the name for each variable in the corresponding model hyperparameter.\n",
    "4. If the model uses future exogenous variables, pass the future dataframe (`futr_df`) to the `predict` method."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Kin G. Olivares, Cristian Challu, Grzegorz Marcjasz, Rafał Weron, Artur Dubrawski, Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx, International Journal of Forecasting](https://www.sciencedirect.com/science/article/pii/S0169207022000413)\n",
    "\n",
    "- [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
